scholarly journals Meta-heterogeneity: evaluating and describing the diversity in glycosylation between sites on the same glycoprotein

2020 ◽  
pp. mcp.R120.002093 ◽  
Author(s):  
Tomislav Caval ◽  
Albert J. R. Heck ◽  
Karli R. Reiding
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Plasma Proteins ◽  
Protein Glycosylation ◽  
Glycopeptide Enrichment ◽  
Technological Advances ◽  
Glycosylation Sites ◽  
The Relationship

Mass spectrometry-based glycoproteomics has gone through some incredible developments over the last few years. Technological advances in glycopeptide enrichment, fragmentation methods, and data analysis workflows have enabled the transition of glycoproteomics from a niche application, mainly focused on the characterization of isolated glycoproteins, to a mature technology capable of profiling thousands of intact glycopeptides at once. In addition to numerous biological discoveries catalyzed by the technology, we are also observing an increase in studies focusing on global protein glycosylation and the relationship between multiple glycosylation sites on the same protein. It has become apparent that just describing protein glycosylation in terms of micro- and macro-heterogeneity, respectively the variation and occupancy of glycans at a given site, is not sufficient to describe the observed interactions between sites. In this perspective we propose a new term, meta-heterogeneity, to describe a higher level of glycan regulation: the variation in glycosylation across multiple sites of a given protein. We provide literature examples of extensive meta-heterogeneity on relevant proteins such as antibodies, erythropoietin, myeloperoxidase and a number of serum and plasma proteins. Furthermore, we postulate on the possible biological reasons and causes behind the intriguing meta-heterogeneity observed in glycoproteins.

scholarly journals Perception of nurses about the risk assessment of the low complexity non-referred user

2020 ◽  
Vol 42 ◽  
pp. e47205
Author(s):  
Aline Buriola ◽  
Camilla Passarela Silva ◽  
Eduardo Fuzetto Cazañas ◽  
Tayomara Ferreira Nascimento
Keyword(s):  
Risk Assessment ◽  
Data Analysis ◽  
Low Complexity ◽  
Risk Classification ◽  
Data Organization ◽  
Healthcare Network ◽  
Nurses Perceptions ◽  
The Relationship ◽  
User Assessment

The goal of this study was to assess the perceptions and behaviors of nurses who provide triage with risk assessment to low complexity non-referred patients. The participants of the study were nurses who were performing patients’ triage with risk assessment, and the sample consisted of thirteen participants. The instruments used for the interviews were semi-structured questionnaires related to the characterization of the topic under study. Content analysis, i.e., the method proposed by Bardin, was used for data analysis. For data organization, we used MAXQDA Analytics Pro 2018, a software program that favored the identification between the similarities of the elements and ideas, thus making it possible to reach the cores of meanings. The identified categories were: (a) understanding about the healthcare provided by the emergency/urgency care Network; (b) evaluation of patient triage with risk classification; and (c) difficulties/challenges observed at the institution when providing user assessment with risk classification. It is concluded that nurses’ perceptions regarding the topic under study were linked to the disarticulation of the healthcare Network, the fragility of the relationship between physicians and nurses, and the lack of use of institutional protocols.

scholarly journals Sensitive and fast characterization of site-specific protein glycosylation with capillary electrophoresis coupled to mass spectrometry

Talanta ◽  
2018 ◽  
Vol 179 ◽  
pp. 22-27 ◽  
Author(s):  
Yanyan Qu ◽  
Liangliang Sun ◽  
Guijie Zhu ◽  
Zhenbin Zhang ◽  
Elizabeth H. Peuchen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Capillary Electrophoresis ◽  
Specific Protein ◽  
Protein Glycosylation ◽  
Site Specific

scholarly journals DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction

Molecules ◽  
2021 ◽  
Vol 26 (23) ◽  
pp. 7314
Author(s):  
Subash C. Pakhrin ◽  
Kiyoko F. Aoki-Kinoshita ◽  
Doina Caragea ◽  
Dukka B. KC
Keyword(s):  
Computational Prediction ◽  
Structural Features ◽  
Glycosylation Site ◽  
Protein Glycosylation ◽  
Evolutionary Information ◽  
Computational Technique ◽  
Post Translational Modification ◽  
Glycosylation Sites ◽  
Better Than

Protein N-linked glycosylation is a post-translational modification that plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of glycosylation sites. Most of the existing predictors for N-linked glycosylation utilize the information that the glycosylation site occurs at the N-X-[S/T] sequon, where X is any amino acid except proline. Not all N-X-[S/T] sequons are glycosylated, thus the N-X-[S/T] sequon is a necessary but not sufficient determinant for protein glycosylation. In that regard, computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem. Here, we report DeepNGlyPred a deep learning-based approach that encodes the positive and negative sequences in the human proteome dataset (extracted from N-GlycositeAtlas) using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionary information. DeepNGlyPred produces SN, SP, MCC, and ACC of 88.62%, 73.92%, 0.60, and 79.41%, respectively on N-GlyDE independent test set, which is better than the compared approaches. These results demonstrate that DeepNGlyPred is a robust computational technique to predict N-Linked glycosylation sites confined to N-X-[S/T] sequon. DeepNGlyPred will be a useful resource for the glycobiology community.

scholarly journals GlypNirO: An automated workflow for quantitative N- and O-linked glycoproteomic data analysis

2020 ◽  
Author(s):  
Toan K. Phung ◽  
Cassandra L. Pegg ◽  
Benjamin L. Schulz
Keyword(s):  
Mass Spectrometry ◽  
Hepatocellular Carcinoma ◽  
Data Analysis ◽  
High Throughput ◽  
Protein Glycosylation ◽  
Software Pipeline ◽  
Site Specific ◽  
Proteome Discoverer ◽  
Automated Software

AbstractMass spectrometry glycoproteomics is rapidly maturing, allowing unprecedented insights into the diversity and functions of protein glycosylation. However, quantitative glycoproteomics remains challenging. We developed GlypNirO, an automated software pipeline which integrates the complementary outputs of Byonic and Proteome Discoverer to allow high-throughput automated quantitative glycoproteomic data analysis. The output of GlypNirO is clearly structured, allowing manual interrogation, and is also appropriate for input into diverse statistical workflows. We used GlypNirO to analyse a published plasma glycoproteome dataset and identified changes in site-specific N- and O-glycosylation occupancy and structure associated with hepatocellular carcinoma as putative biomarkers of disease.

scholarly journals GlypNirO: An automated workflow for quantitative N- and O-linked glycoproteomic data analysis

2020 ◽  
Vol 16 ◽  
pp. 2127-2135 ◽  
Author(s):  
Toan K Phung ◽  
Cassandra L Pegg ◽  
Benjamin L Schulz
Keyword(s):  
Mass Spectrometry ◽  
Hepatocellular Carcinoma ◽  
Data Analysis ◽  
High Throughput ◽  
Protein Glycosylation ◽  
Software Pipeline ◽  
Site Specific ◽  
Proteome Discoverer ◽  
Automated Software

Mass spectrometry glycoproteomics is rapidly maturing, allowing unprecedented insights into the diversity and functions of protein glycosylation. However, quantitative glycoproteomics remains challenging. We developed GlypNirO, an automated software pipeline which integrates the complementary outputs of Byonic and Proteome Discoverer to allow high-throughput automated quantitative glycoproteomic data analysis. The output of GlypNirO is clearly structured, allowing manual interrogation, and is also appropriate for input into diverse statistical workflows. We used GlypNirO to analyse a published plasma glycoproteome dataset and identified changes in site-specific N- and O-glycosylation occupancy and structure associated with hepatocellular carcinoma as putative biomarkers of disease.

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